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# bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R

David Ardia; Lennart F. Hoogerheide

### Citation Style Language JSON Export

{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.231327",
"container_title": "The R Journal",
"title": "bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R",
"issued": {
"date-parts": [
[
2017,
1,
5
]
]
},
"abstract": "<p>The package bayesGARCH implements in R (R Core Team, 2016) the Bayesian estimation procedure described in Ardia (2008, chapter 5) for the GARCH(1,1) model with Student-t innovations. The approach consists of a Metropolis-Hastings (MH) algorithm where the proposal distributions are constructed from auxiliary ARMA processes on the squared observations. This methodology avoids the time-consuming and difficult task, especially for non-experts, of choosing and tuning a sampling algorithm. We refer the user to Ardia (2008) and Ardia and Hoogerheide (2010) for illustrations. The latest version of the package is available at https://github.com/ArdiaD/bayesGARCH.</p>",
"author": [
{
"family": "David Ardia"
},
{
"family": "Lennart F. Hoogerheide"
}
],
"page": "41-47",
"volume": "2",
"version": "v2.0.4",
"type": "article",
"issue": "2",
"id": "231327"
}
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